Manufacturing intelligence systems provide graphical representations of information models that are multidimensional data models to represent business logic and relationships between various unstructured source data items. Conventional systems configure information models to run in accordance with a predetermined schedule to periodically extract, transform, and load data from source systems to an intelligence data store. Periodically executing objects in accordance with a predetermined schedule may lead to decreased performance. For example, scheduling object execution too frequently results in extra and unnecessary utilization of processor resources and network bandwidth. Scheduling object execution too infrequently results in data not being available for analysis for a period of time. The static and rigid nature of a schedule is not flexible enough to handle unpredictable events that cannot be scheduled, such as reconciliation of past data due to errors, unexpected beginning and/or end of work orders, incorrect data entry that has been corrected in a source system, and the like.